Triple
T2103593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tisza |
E37143
|
entity |
| Predicate | flowsThroughCity |
P10456
|
FINISHED |
| Object | Szeged |
E37566
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Szeged | Statement: [Tisza, flowsThroughCity, Szeged]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Szeged Context triple: [Tisza, flowsThroughCity, Szeged]
-
A.
Szeged
chosen
Szeged is a prominent city in southern Hungary known for its university, paprika production, and distinctive Art Nouveau architecture.
-
B.
Debrecen
Debrecen is Hungary’s second-largest city and a key cultural, economic, and educational center in the country’s eastern region.
-
C.
Kaposvár
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
D.
Kecskemét
Kecskemét is a city in central Hungary known for its Art Nouveau architecture, cultural institutions, and role as an administrative and economic center of the region.
-
E.
Miskolc
Miskolc is a large industrial and cultural city in northeastern Hungary, known for its steel industry, historic center, and nearby cave baths.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a8861828948190924aa30c08806b3a |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abbabf7cdc81909636dff34badc1c5 |
completed | March 7, 2026, 5:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afce742d288190bfdcffb81c29a173 |
completed | March 10, 2026, 7:55 a.m. |
Created at: March 4, 2026, 7:43 p.m.